Marine Science Institute, University of the Philippines Diliman, Quezon City 1101, Philippines.
Marine Science Institute, University of the Philippines Diliman, Quezon City 1101, Philippines; School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, 123 Cheomdangwagiro, Bukgu, Gwangju 61005, Republic of Korea.
Sci Total Environ. 2020 Mar 10;707:136173. doi: 10.1016/j.scitotenv.2019.136173. Epub 2019 Dec 17.
Harmful algal blooms (HABs) that produce toxins and those that lead to fish kills are global problems that appear to be increasing in frequency and expanding in area. One way to help mitigate their impacts on people's health and livelihoods is to develop early-warning systems. Models to predict and manage HABs typically make use of complex multi-model structures incorporating satellite imagery and frequent monitoring data with different levels of detail into hydrodynamic models. These relatively more sophisticated methods are not necessarily applicable in countries like the Philippines. Empirical statistical models can be simpler alternatives that have also been successful for HAB forecasting of toxic blooms. Here, we present the use of the random forest, a machine learning algorithm, to develop an early-warning system for the prediction of two different types of HABs: fish kill and toxic bloom occurrences in Bolinao-Anda, Philippines, using data that can be obtained from in situ sensors. This site features intensive and extensive mariculture activities, as well as a long history of HABs. Data on temperature, salinity, dissolved oxygen, pH and chlorophyll from 2015 to 2017 were analyzed together with shellfish ban and fish kill occurrences. The random forest algorithm performed well: the fish kill and toxic bloom models were 96.1% and 97.8% accurate in predicting fish kill and shellfish ban occurrences, respectively. For both models, the most important predictive variable was a decrease in dissolved oxygen. Fish kills were more likely during higher salinity and temperature levels, whereas the toxic blooms occurred more at relatively lower salinity and higher chlorophyll conditions. This demonstrates a step towards integrating information from data that can be obtained through real-time sensors into a an early-warning system for two different types of HABs. Further testing of these models through times and different areas are recommended.
有害藻华（HAB）产生毒素和导致鱼类死亡是全球性问题，其发生频率似乎越来越高，影响范围也越来越大。帮助减轻其对人类健康和生计的影响的一种方法是开发预警系统。预测和管理 HAB 的模型通常利用复杂的多模型结构，将卫星图像和不同详细程度的频繁监测数据纳入水动力模型。这些相对更复杂的方法在菲律宾等国家不一定适用。经验统计模型可以是更简单的替代方法，对于有毒藻华的预测也取得了成功。在这里，我们介绍了随机森林（一种机器学习算法）的使用，该算法用于使用可以从现场传感器获得的数据，在菲律宾 Bolinao-Anda 开发预测两种不同类型的 HAB（鱼类死亡和有毒藻华发生）的预警系统。该地点的海水养殖活动密集且广泛，并且具有悠久的 HAB 历史。分析了 2015 年至 2017 年期间的温度、盐度、溶解氧、pH 值和叶绿素数据以及贝类禁捕和鱼类死亡事件。随机森林算法表现良好：鱼类死亡和有毒藻华模型分别在预测鱼类死亡和贝类禁捕事件方面的准确率为 96.1%和 97.8%。对于这两个模型，最重要的预测变量是溶解氧的减少。在高盐度和高温水平下，鱼类死亡的可能性更大，而在相对低盐度和高叶绿素条件下，有毒藻华发生的可能性更大。这表明了通过实时传感器获取的数据将信息整合到两种不同类型的 HAB 预警系统中的一个步骤。建议通过不同的时间和区域进一步测试这些模型。
